Neuroscience incorporates knowledge from a range of scales from molecular dynamics to neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Systems biology, for instance, is among the more standardized fields. However, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implement custom-made MATLAB; scripts to perform parameter estimation and sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. By this we can simulate the same model in three principally different simulators, describing different aspects of the system, and with a smooth conversion between the different model formats.

中文翻译：

---

系统生物学和神经科学中用于模型构建，分析和参数估计的模块化工作流程

神经科学融合了从分子动力学到神经网络的各种规模的知识。建模是了解单个规模过程或两个相邻规模之间相互作用的宝贵工具，研究人员在模型构建和分析过程中使用了多种不同的软件工具。例如，系统生物学属于更为标准化的领域。但是，不同模型格式之间的转换以及各种工具之间的互操作性仍然有些问题。为了解决这些缺点并牢记FAIR（可搜索性，可访问性，互操作性，可重用性）数据原则，我们已经开发了一种工作流程，用于使用现有工具构建和分析生化途径模型，这些工具可以在开发的所有阶段用于模型的存储和完善。我们选择了SBtab格式，该格式允许将生化模型和相关数据存储在单个文件中，并提供一组易于阅读的语法规则。接下来，我们实现定制的MATLAB。脚本来执行模型优化中使用的参数估计和敏感性分析。此外，我们为生化模型开发了一个基于Web的应用程序，该应用程序允许使用无网络求解器或随机求解器并结合几何图形进行模拟。最后，我们通过在NEURON中运行多尺度模拟来说明在生物物理学上详细的单神经元模型中生化模型的可转换性和使用。